Introduction to Modelling of Cognitive Prosesses

Informacje ogólne

Kod przedmiotu:	2500-KOG-PL-OBW-1
Kod Erasmus / ISCED:	14.4 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0313) Psychologia Kod ISCED - Międzynarodowa Standardowa Klasyfikacja Kształcenia (International Standard Classification of Education) została opracowana przez UNESCO.
Nazwa przedmiotu:	Introduction to Modelling of Cognitive Prosesses
Jednostka:	Wydział Psychologii
Grupy:	Kognitywistyka 2
Punkty ECTS i inne:	(brak) Podstawowe informacje o zasadach przyporządkowania punktów ECTS: roczny wymiar godzinowy nakładu pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się dla danego etapu studiów wynosi 1500-1800 h, co odpowiada 60 ECTS; tygodniowy wymiar godzinowy nakładu pracy studenta wynosi 45 h; 1 punkt ECTS odpowiada 25-30 godzinom pracy studenta potrzebnej do osiągnięcia zakładanych efektów uczenia się; tygodniowy nakład pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się pozwala uzyskać 1,5 ECTS; nakład pracy potrzebny do zaliczenia przedmiotu, któremu przypisano 3 ECTS, stanowi 10% semestralnego obciążenia studenta. zobacz reguły punktacji
Język prowadzenia:	angielski
Skrócony opis:	(tylko po angielsku) The course introduces students to various approaches to the modeling cognitive systems, provides a broad overview of modeling methods and their applications, and discusses cognitive modeling in general, from the dynamical systems perspective.
Pełny opis:	(tylko po angielsku) Cognitive systems are characterized by their ability to functionally adapt to their environments, which in turn allows them to react to the changes in their surroundings accordingly or initiate actions of their own. Mechanisms of functional adaptation of this kind are found in a wide variety of phenomena spanning multiple scales: biological systems (single cells, cell colonies, organized tissues, systems such as immune system etc.), whole organisms, higher animals and humans with their mental processes, social groups exhibiting cultural adaptation, and artificial systems (autonomous robots, software agents). Modeling such phenomena requires an interdisciplinary approach in which different fields of study stimulate each other: psychological and biological discoveries inspire the development of new mathematical models and computational methods, which often find applications outside of the original domain. Developed models help to formulate the hypotheses, plan further experiments, verify theories, and augment the overall understanding of cognitive processes. The aim of this course is to give an overview of various paradigms, approaches and methods used to model processes of systemic adaptation. We show how different methods relate to each other and how they can be applied to uncover different aspects of studied phenomena. We focus on methodological issues and illustrate them with examples of concrete models and concrete research from multiple domains such as motor development, decision making, language acquisition, social coordination, cultural evolution etc.
Literatura:	1. Paradigms of cognitive modeling. Symbolic and dynamic approaches. 2. Dynamical systems I. Dynamical systems in modeling cognition. Synergy. a. Guastello, S.J. & Liebovitch, L.S. (2011) Introduction to non-linear dynamics and complexity. In: Chaos and Complexity in Psychology. CUP. b. Kelso, J. A. S., Tuller, B., Bateson, E.-V., & Fowler, C. A. (1984). Functionally specific articulatory cooperation following jaw perturbations during speech: Evidence for coordinative structures. Journal of Experimental Psychology: Human Perception and Performance, 10, 812–832. 3. Dynamical systems II. Dynamical systems in modeling cognition. Haken-Kelso-Bunz model of motor coordination. a. http://www.scholarpedia.org/article/Haken-Kelso-Bunz_model 4. Describing systems dynamics. Difference equations, differential equations, phase spaces, attractors. a. Richardson, M., Dale, R., & Marsh, K. (2014). Complex Dynamical Systems in Social and Personality Psychology. In H. Reis & C. Judd (Eds.), Handbook of Research Methods in Social and Personality Psychology. Cambridge: Cambridge Univ. Press 5. Computational approaches to modeling complexity. Formal languages and automata, information theory. a. A. Salomaa, Computation and automata, Cambridge Univ.Press, 1985. (selected parts) 6. Logic-based models of cognition. Production rules. Belief–desire–intention model of reasoning. a. Bringsjord, S. (2008). Declarative/Logic-Based Cognitive Modeling. In R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (Cambridge Handbooks in Psychology, pp. 127-169). Cambridge: Cambridge Univ. Press 7. Evolution of artificial intelligence: a case study. 8. Statistical models I. Statistical models of rationality. Ecological rationality and bias-variance dilemma. Stochastic processes. a. Gigerenzer, G., & Brighton, H. (2009). Homo Heuristicus: Why Biased Minds Make Better Inferences. Topics in Cognitive Science, 1(1), 107–143. 9. Statistical models II. Modeling uncertainty with statistical models. Introduction to Bayesian modeling. a. Leo Breiman et al. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16 (3):199–231, 2001. b. Lee, M. D., & Wagenmakers, E.-J. (2014). Bayesian Cognitive Modeling: A Practical Course. Cambridge ; New York: Cambridge University Press. c. Kruschke, J. (2015), Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan; Academic Press / Elsevier. 10. Statistical models III. Bayesian methods in modeling action and perception. Free Energy Minimization. a. Mathys C, Jean Daunizeau J, Karl J Friston KJ, Klaas Enno Stephan KE. (2011) A Bayesian foundation for individual learning under uncertainty Front. Hum. Neurosci. 5:35 b. Karl Friston, Christopher Thornton, and Andy Clark. Free-energy minimization and the dark-room problem. Frontiers in Psychology, 3:130, 2012. c. Karl J Friston, Jean Daunizeau, and Stefan J Kiebel. Reinforcement learning or active inference? PloS one, 4(7): e6421, 2009. 11. Neural networks I. Neural networks as universal models of nonlinear dynamics. Feedforward and recurrent architectures. a. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning, MIT Press. 12. Neural networks II. Modeling language acquisition and linguistic interaction with neural networks. a. Thomas, M., & McClelland, J. (2008). Connectionist Models of Cognition. In R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (Cambridge Handbooks in Psychology, pp. 23-58). Cambridge: Cambridge University Press. 13. Evolutionary computation. Models of language evolution, evolutionary robotics. a. Puglisi A., Baronchelli, A. & Loreto, V. (2008). Cultural route to the emergence of linguistic categories Proc. Natl. Acad. Sci. USA 105, 7936 b. Steels, L. and Belpaeme, T. (2005). Coordinating Perceptually Grounded Categories through Language: A Case Study for Colour. Behavioral and Brain Sciences, 28(4), 469-89; c. Kenneth A. De Jong. Evolutionary Computation: A Unified Approach. MIT Press, Cambridge, Massachusetts, 2006 14. Modern systems biology. a. D. Noble, “Biophysics and systems biology,” Society, 2010, pp. 1125-1139. 15. General systems theory. Course summary.
Efekty uczenia się:	(tylko po angielsku) Upon successful completion of the course you will: - understand the theory behind paradigms of cognitive modeling, their origins and assumptions - know multiple methods used to model cognitive systems, understand their strengths and weaknesses - be able to asses the use of cognitive models in scientific literature - be able to communicate concepts related to cognitive modeling within an interdisciplinary team
Metody i kryteria oceniania:	(tylko po angielsku) Attendance to the lecture is obligatory, 2 unexcused absences are allowed. Assessment: Written exam covering the lectures and selected literature. Additionally, students can improve their grade by completing 2-3 homework assignments.

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Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski.